By Chris Ganje, CEO, AMPLYFI
A decade on from the financial crisis, the European banking industry is facing another potentially transformative event – Britain’s decision to leave the EU. With so much uncertainty approaching in 2019, Chris Ganje, CEO and founder of AI-powered business intelligence firm AMPLYFI, looks at how the need for applied technology in analytics has never been greater.
Is Brexit the greatest opportunity or the greatest threat to banking in modern times? Depending on your viewpoint, there will be a statistic to support it. For every dire economic projection, there is a trade deal; for every bank relocation to Frankfurt or Paris, there is an argument that London will continue as one of the world’s leading financial centres.
With such a significant event looming and with so much still unknown, it is more important than ever for senior leaders to make better informed decisions. Decisions on investment, acquisitions, portfolio management, resource or R&D spend can have a profound and long-lasting effect on the performance of any bank.
It’s no surprise that analytics teams have grown in importance in recent years, in direct correlation to the rise of technological applications. The more data we have, the more educated the decisions we can make. Leaders realise that robust business intelligence is the key to making good choices.
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However, human analysis is significantly challenged when it comes to investigating or predicting disruptive events. Humans naturally bring unconscious bias to their assessments – the search terms they use, the categories they filter by, the findings they choose to highlight, and the recommendations they propose will all be impacted by that individual’s own viewpoints and experiences. Take Brexit as an obvious example: presented with all the same information, different groups have reached different conclusions resulting in polarised views of the future and decisions today.
Over the next 12 months and beyond, banks are looking to make sense of something that has no obvious pattern or previous event on which to base their insights. For many, it is a leap into the unknown. Essentially, take your best guess based on your own experiences. In times of uncertainty and risk, senior leaders are liable to trust their gut instinct rather than data. These biased insights and decisions can be catastrophic if they are not challenged robustly.
This is where technology is having a hugely positive influence. Artificial intelligence and machine learning is providing a level of clarity for decision makers that has to date been lacking.
A machine has no preconceived notions of Brexit, no political leanings, and no expectations of what it might uncover. Therefore, it can analyse datasets and present them to analysts in a way that is free from bias. By integrating the very best of machine-driven data acquisition and analytics with human acumen and intuition, banks will be able to see unknown patterns and receive warning signals of disruption much quicker and more effectively than from traditional business intelligence methods.
Through our work with the banking industry at AMPLYFI, we are seeing a shift. Decision makers are moving from a position of scepticism in the value of big data and new technologies, to a realisation that modern capabilities can provide institutions with an edge over their competitors. Most large banks now have an internal AI function whose job it is to harness its power and apply it across the board from analysts to relationship managers through to strategic decision makers.
So, what does this increased understanding and use of technology mean for the banking industry going forward?
A key point to make is that, rather than replace humans, AI is enabling knowledge workers to undertake their work at a speed, volume, and complexity that far exceeds traditional practices. This elevates the role and impact of roles such analysts as they spend more time on higher value-add activities.
Importantly, AI-powered insights can identify trends that analysts would not have thought to mine for and reveal non-obvious connections. We call these the “unknown unknowns” – how can we search for a trend or emerging technology that we don’t even know exists? How can we make connections between variables that we are unaware of? AI has the power to do this for us. Extending the horizon-scanning and future disruption identification capability of analysts from just a handful of years to potentially decades is transformative.
This increased foresight, powered by AI, will provide greater stability for the industry. Banks will be able to identify new opportunities and warning signals much earlier than previously possible. In addition, they will react more quickly to the insights and implement action that is better informed. Even the largest and most traditional institutions can become much more nimble.
What is perhaps most exciting is that we are just at the start of this journey. In only a few years, the whole outlook of the banking industry has changed. There is an emerging ethos that disruption is to be embraced, and the largest institutions want to find the ways that mean they are the first to embrace it.
In many industries – not just banking – incumbents have moved from a position of protectionism to a realisation that if there is a wave, they need to be on it. The next generation of business intelligence, powered by AI and machine learning, is their ride.